Update app.py

app.py

@@ -62,47 +62,40 @@ def audio_tag(
     label = labels[sorted_indexes[0]]
     return formatted_message(format_classname(label), human_input)
 
+def format_classname(classname):
+  return classname.capitalize()
     
-
 def formatted_message(audio_class, human_input):
-    global cached_audio_class
-    if cached_audio_class != audio_class:
-        cached_audio_class = audio_class
-        prefix = f"""You are going to act as a magical tool that allows for humans to communicate with non-human entities like 
-    rocks, crackling fire, trees, animals, and the wind. In order to do this, we're going to provide you the human's text input for the conversation. 
-    The goal is for you to embody that non-human entity and converse with the human.
-    
-    Examples:
-    
-    Non-human Entity: Tree 
-    Human Input: Hello tree
-    Tree: Hello human, I am a tree 
-    
-    Let's begin:
-    Non-human Entity: {audio_class}"""
-        suffix = f'''{{history}}
-    Human Input: {{human_input}}
-    {audio_class}:'''
+    formatted_classname = format_classname(audio_class)
+    if cached_audio_class != formatted_classname:
+        
+        cached_audio_class = formatted_classname
     
-        suffix = f'''Source: {audio_class}
-        Length of Audio in Seconds: {audio_length} 
-        Human Input: {userText}
-        {audio_class} Response:'''
+        prefix = f"""You are going to act as a magical tool that allows for humans to communicate with non-human entities like rocks, crackling fire, trees, animals, and the wind. In order to do this, we're going to provide you the human's text input for the conversation. The goal is for you to embody that non-human entity and converse with the human.
+Examples:
+Non-human Entity: Tree
+Human Input: Hello tree
+Tree: Hello human, I am a tree
+Let's begin:
+Non-human Entity: {formatted_classname}"""
+        suffix = f'''
+{{history}}
+Human Input: {{human_input}}
+{formatted_classname}:'''
         template = prefix + suffix
-    
+
         prompt = PromptTemplate(
             input_variables=["history", "human_input"], 
             template=template
         )
-    
+
         chatgpt_chain = LLMChain(
             llm=OpenAI(temperature=.5, openai_api_key=session_token), 
             prompt=prompt, 
             verbose=True, 
-            memory=ConversationalBufferWindowMemory(k=2, ai_prefix=audio_class),
+            memory=ConversationalBufferWindowMemory(k=2, ai=formatted_classname),
         )
-
-    output = chatgpt_chain.predict(human_input=message)
+    output = chatgpt_chain.predict(human_input=human_input)
 
     return output